Use native ops for PixArt

PixArt/config.py

@@ -93,8 +93,7 @@ def model_config_from_unet(sd):
                 config["pe_interpolation"] = 1
     model_config = PixArtConfig(config)
     model_config.unet_class = model_class
-    logging.info(f"Detected PixArt model as [{model_class}]")
+    logging.debug(f"PixArt config: {model_class}\n{config}")
-    logging.info(f"PixArt config:\n{config}")
     return model_config
 
 resolutions = {

PixArt/loader.py

@@ -19,14 +19,8 @@ class PixArtModel(comfy.model_base.BaseModel):
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
 
-        img_hw = kwargs.get("img_hw", None)
+        for name in ["width", "height", "aspect_ratio", "img_hw"]: # TODO: remove last one
-        if img_hw is not None:
+            out[name] = comfy.conds.CONDRegular(torch.tensor(name))
-            out["img_hw"] = comfy.conds.CONDRegular(torch.tensor(img_hw))
-
-        aspect_ratio = kwargs.get("aspect_ratio", None)
-        if aspect_ratio is not None:
-            out["aspect_ratio"] = comfy.conds.CONDRegular(torch.tensor(aspect_ratio))
-
         return out
 
 def load_pixart_state_dict(sd, model_options={}):
@@ -49,7 +43,7 @@ def load_pixart_state_dict(sd, model_options={}):
     load_device = model_management.get_torch_device()
     offload_device = comfy.model_management.unet_offload_device()
 
-    dtype = model_options.get("dtype", torch.float16) # TODO: fix this
+    dtype = model_options.get("dtype", None)
     weight_dtype = comfy.utils.weight_dtype(sd)
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
 

PixArt/model/blocks.py

@@ -12,9 +12,10 @@ import math
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from timm.models.vision_transformer import Mlp, Attention as Attention_
 from einops import rearrange
 
+from .utils import to_2tuple
+
 sdpa_32b = None
 Q_4GB_LIMIT = 32000000
 """If q is greater than this, the operation will likely require >4GB VRAM, which will fail on Intel Arc Alchemist GPUs without a workaround."""
@@ -44,7 +45,7 @@ def t2i_modulate(x, shift, scale):
     return x * (1 + scale) + shift
 
 class MultiHeadCrossAttention(nn.Module):
-    def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., **block_kwargs):
+    def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., dtype=None, device=None, operations=None, **block_kwargs):
         super(MultiHeadCrossAttention, self).__init__()
         assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
 
@@ -52,10 +53,10 @@ class MultiHeadCrossAttention(nn.Module):
         self.num_heads = num_heads
         self.head_dim = d_model // num_heads
 
-        self.q_linear = nn.Linear(d_model, d_model)
+        self.q_linear = operations.Linear(d_model, d_model, dtype=dtype, device=device)
-        self.kv_linear = nn.Linear(d_model, d_model*2)
+        self.kv_linear = operations.Linear(d_model, d_model*2, dtype=dtype, device=device)
         self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(d_model, d_model)
+        self.proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
         self.proj_drop = nn.Dropout(proj_drop)
 
     def forward(self, x, cond, mask=None):
@@ -111,7 +112,7 @@ class MultiHeadCrossAttention(nn.Module):
         return x
 
 
-class AttentionKVCompress(Attention_):
+class AttentionKVCompress(nn.Module):
     """Multi-head Attention block with KV token compression and qk norm."""
 
     def __init__(
@@ -122,6 +123,9 @@ class AttentionKVCompress(Attention_):
         sampling='conv',
         sr_ratio=1,
         qk_norm=False,
+        dtype=None,
+        device=None,
+        operations=None,
         **block_kwargs,
     ):
         """
@@ -130,19 +134,26 @@ class AttentionKVCompress(Attention_):
             num_heads (int): Number of attention heads.
             qkv_bias (bool:  If True, add a learnable bias to query, key, value.
         """
-        super().__init__(dim, num_heads=num_heads, qkv_bias=qkv_bias, **block_kwargs)
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
 
         self.sampling=sampling    # ['conv', 'ave', 'uniform', 'uniform_every']
         self.sr_ratio = sr_ratio
         if sr_ratio > 1 and sampling == 'conv':
             # Avg Conv Init.
-            self.sr = nn.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio)
+            self.sr = operations.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio, dtype=dtype, device=device)
-            self.sr.weight.data.fill_(1/sr_ratio**2)
+            # self.sr.weight.data.fill_(1/sr_ratio**2)
-            self.sr.bias.data.zero_()
+            # self.sr.bias.data.zero_()
-            self.norm = nn.LayerNorm(dim)
+            self.norm = operations.LayerNorm(dim, dtype=dtype, device=device)
         if qk_norm:
-            self.q_norm = nn.LayerNorm(dim)
+            self.q_norm = operations.LayerNorm(dim, dtype=dtype, device=device)
-            self.k_norm = nn.LayerNorm(dim)
+            self.k_norm = operations.LayerNorm(dim, dtype=dtype, device=device)
         else:
             self.q_norm = nn.Identity()
             self.k_norm = nn.Identity()
@@ -204,14 +215,12 @@ class AttentionKVCompress(Attention_):
         if model_management.xformers_enabled():
             x = xformers.ops.memory_efficient_attention(
                 q, k, v,
-                p=self.attn_drop.p,
+                p=0,
                 attn_bias=attn_bias
             )
         else:
             q, k, v = map(lambda t: t.transpose(1, 2),(q, k, v),)
-
+            p = 0
-            p = getattr(self.attn_drop, "p", 0) # IPEX.optimize() will turn attn_drop into an Identity()
-            
             if sdpa_32b is not None and (q.element_size() * q.nelement()) > Q_4GB_LIMIT:
                 sdpa = sdpa_32b
             else:
@@ -224,30 +233,6 @@ class AttentionKVCompress(Attention_):
             ).transpose(1, 2).contiguous()
         x = x.view(B, N, C)
         x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-#################################################################################
-#   AMP attention with fp32 softmax to fix loss NaN problem during training     #
-#################################################################################
-class Attention(Attention_):
-    def forward(self, x):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
-        use_fp32_attention = getattr(self, 'fp32_attention', False)
-        if use_fp32_attention:
-            q, k = q.float(), k.float()
-        with torch.cuda.amp.autocast(enabled=not use_fp32_attention):
-            attn = (q @ k.transpose(-2, -1)) * self.scale
-            attn = attn.softmax(dim=-1)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
         return x
 
 
@@ -256,13 +241,13 @@ class FinalLayer(nn.Module):
     The final layer of PixArt.
     """
 
-    def __init__(self, hidden_size, patch_size, out_channels):
+    def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
         self.adaLN_modulation = nn.Sequential(
             nn.SiLU(),
-            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+            operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
         )
 
     def forward(self, x, c):
@@ -271,23 +256,23 @@ class FinalLayer(nn.Module):
         x = self.linear(x)
         return x
 
-
 class T2IFinalLayer(nn.Module):
     """
     The final layer of PixArt.
     """
 
-    def __init__(self, hidden_size, patch_size, out_channels):
+    def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
         self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5)
         self.out_channels = out_channels
 
     def forward(self, x, t):
+        dtype = x.dtype
         shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1)
         x = t2i_modulate(self.norm_final(x), shift, scale)
-        x = self.linear(x)
+        x = self.linear(x.to(dtype))
         return x
 
 
@@ -296,13 +281,13 @@ class MaskFinalLayer(nn.Module):
     The final layer of PixArt.
     """
 
-    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels):
+    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6)
+        self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.linear = nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
         self.adaLN_modulation = nn.Sequential(
             nn.SiLU(),
-            nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True)
+            operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device)
         )
     def forward(self, x, t):
         shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
@@ -316,13 +301,13 @@ class DecoderLayer(nn.Module):
     The final layer of PixArt.
     """
 
-    def __init__(self, hidden_size, decoder_hidden_size):
+    def __init__(self, hidden_size, decoder_hidden_size, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_decoder = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.norm_decoder = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True)
+        self.linear = operations.Linear(hidden_size, decoder_hidden_size, bias=True, dtype=dtype, device=device)
         self.adaLN_modulation = nn.Sequential(
             nn.SiLU(),
-            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+            operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
         )
     def forward(self, x, t):
         shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
@@ -339,12 +324,12 @@ class TimestepEmbedder(nn.Module):
     Embeds scalar timesteps into vector representations.
     """
 
-    def __init__(self, hidden_size, frequency_embedding_size=256):
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
         super().__init__()
         self.mlp = nn.Sequential(
-            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
             nn.SiLU(),
-            nn.Linear(hidden_size, hidden_size, bias=True),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
         )
         self.frequency_embedding_size = frequency_embedding_size
 
@@ -368,9 +353,9 @@ class TimestepEmbedder(nn.Module):
             embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
         return embedding
 
-    def forward(self, t):
+    def forward(self, t, dtype):
         t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
-        t_emb = self.mlp(t_freq.to(t.dtype))
+        t_emb = self.mlp(t_freq.to(dtype))
         return t_emb
 
 
@@ -379,12 +364,12 @@ class SizeEmbedder(TimestepEmbedder):
     Embeds scalar timesteps into vector representations.
     """
 
-    def __init__(self, hidden_size, frequency_embedding_size=256):
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
-        super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size)
+        super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size, operations=operations)
         self.mlp = nn.Sequential(
-            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
             nn.SiLU(),
-            nn.Linear(hidden_size, hidden_size, bias=True),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
         )
         self.frequency_embedding_size = frequency_embedding_size
         self.outdim = hidden_size
@@ -409,10 +394,10 @@ class LabelEmbedder(nn.Module):
     Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
     """
 
-    def __init__(self, num_classes, hidden_size, dropout_prob):
+    def __init__(self, num_classes, hidden_size, dropout_prob, dtype=None, device=None, operations=None):
         super().__init__()
         use_cfg_embedding = dropout_prob > 0
-        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
+        self.embedding_table = operations.Embedding(num_classes + use_cfg_embedding, hidden_size, dtype=dtype, device=device),
         self.num_classes = num_classes
         self.dropout_prob = dropout_prob
 
@@ -434,15 +419,31 @@ class LabelEmbedder(nn.Module):
         embeddings = self.embedding_table(labels)
         return embeddings
 
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+
+        self.fc1 = operations.Linear(in_features, hidden_features, bias=True, dtype=dtype, device=device)
+        self.act = act_layer()
+        self.fc2 = operations.Linear(hidden_features, out_features, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.act(self.fc1(x))
+        return self.fc2(x)
 
 class CaptionEmbedder(nn.Module):
     """
     Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
     """
 
-    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120):
+    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None):
         super().__init__()
-        self.y_proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0)
+        self.y_proj = Mlp(
+            in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer,
+            dtype=dtype, device=device, operations=operations,
+        )
         self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5))
         self.uncond_prob = uncond_prob
 
@@ -472,9 +473,12 @@ class CaptionEmbedderDoubleBr(nn.Module):
     Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
     """
 
-    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120):
+    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None):
         super().__init__()
-        self.proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0)
+        self.proj = Mlp(
+            in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer,
+            dtype=dtype, device=device, operations=operations,
+        )
         self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5)
         self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5)
         self.uncond_prob = uncond_prob

PixArt/model/pixartms.py

@@ -11,11 +11,9 @@
 import torch
 import torch.nn as nn
 from tqdm import tqdm
-from timm.models.layers import DropPath
-from timm.models.vision_transformer import Mlp
 
 from .utils import auto_grad_checkpoint, to_2tuple
-from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder
+from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder, Mlp
 from .pixart import PixArt, get_2d_sincos_pos_embed
 
 
@@ -31,12 +29,15 @@ class PatchEmbed(nn.Module):
             norm_layer=None,
             flatten=True,
             bias=True,
+            dtype=None,
+            device=None,
+            operations=None
     ):
         super().__init__()
         patch_size = to_2tuple(patch_size)
         self.patch_size = patch_size
         self.flatten = flatten
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+        self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
         self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
 
     def forward(self, x):
@@ -52,29 +53,34 @@ class PixArtMSBlock(nn.Module):
     A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
     """
     def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,
-                 sampling=None, sr_ratio=1, qk_norm=False, **block_kwargs):
+                 sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs):
         super().__init__()
         self.hidden_size = hidden_size
-        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
         self.attn = AttentionKVCompress(
             hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
-            qk_norm=qk_norm, **block_kwargs
+            qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs
         )
-        self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs)
+        self.cross_attn = MultiHeadCrossAttention(
-        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+            hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs
+        )
+        self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
         # to be compatible with lower version pytorch
         approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0)
+        self.mlp = Mlp(
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+            in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu,
+            dtype=dtype, device=device, operations=operations
+        )
         self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
 
     def forward(self, x, y, t, mask=None, HW=None, **kwargs):
         B, N, C = x.shape
+        dtype = x.dtype
 
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(x.dtype) + t.reshape(B, 6, -1)).chunk(6, dim=1)
-        x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
+        x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
         x = x + self.cross_attn(x, y, mask)
-        x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
+        x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
 
         return x
 
@@ -105,40 +111,52 @@ class PixArtMS(PixArt):
             micro_condition=True,
             qk_norm=False,
             kv_compress_config=None,
+            dtype=None,
+            device=None,
+            operations=None,
             **kwargs,
     ):
-        super().__init__(
+        nn.Module.__init__(self)
-            input_size=input_size,
+        self.dtype = dtype
-            patch_size=patch_size,
+        self.pred_sigma = pred_sigma
-            in_channels=in_channels,
+        self.in_channels = in_channels
-            hidden_size=hidden_size,
+        self.out_channels = in_channels * 2 if pred_sigma else in_channels
-            depth=depth,
+        self.patch_size = patch_size
-            num_heads=num_heads,
+        self.num_heads = num_heads
-            mlp_ratio=mlp_ratio,
+        self.pe_interpolation = pe_interpolation
-            class_dropout_prob=class_dropout_prob,
+        self.pe_precision = pe_precision
-            learn_sigma=learn_sigma,
+        self.depth = depth
-            pred_sigma=pred_sigma,
+
-            drop_path=drop_path,
-            pe_interpolation=pe_interpolation,
-            config=config,
-            model_max_length=model_max_length,
-            qk_norm=qk_norm,
-            kv_compress_config=kv_compress_config,
-            **kwargs,
-        )
-        self.dtype = torch.get_default_dtype()
         self.h = self.w = 0
         approx_gelu = lambda: nn.GELU(approximate="tanh")
         self.t_block = nn.Sequential(
             nn.SiLU(),
-            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
+            operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
         )
-        self.x_embedder = PatchEmbed(patch_size, in_channels, hidden_size, bias=True)
+        self.x_embedder = PatchEmbed(
-        self.y_embedder = CaptionEmbedder(in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, act_layer=approx_gelu, token_num=model_max_length)
+            patch_size, in_channels, hidden_size, bias=True,
+            dtype=dtype, device=device, operations=operations
+        )
+        self.t_embedder = TimestepEmbedder(
+            hidden_size, dtype=dtype, device=device, operations=operations,
+        )
+        self.y_embedder = CaptionEmbedder(
+            in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,
+            act_layer=approx_gelu, token_num=model_max_length,
+            dtype=dtype, device=device, operations=operations,
+        )
+
         self.micro_conditioning = micro_condition
         if self.micro_conditioning:
-            self.csize_embedder = SizeEmbedder(hidden_size//3)  # c_size embed
+
-            self.ar_embedder = SizeEmbedder(hidden_size//3)     # aspect ratio embed
+            self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
+            self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
+
+        # Will use fixed sin-cos embedding:
+        num_patches = (input_size // patch_size) * (input_size // patch_size)
+        self.base_size = input_size // self.patch_size
+        self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
+
         drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
         if kv_compress_config is None:
             kv_compress_config = {
@@ -153,12 +171,17 @@ class PixArtMS(PixArt):
                 sampling=kv_compress_config['sampling'],
                 sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,
                 qk_norm=qk_norm,
+                dtype=dtype,
+                device=device,
+                operations=operations,
             )
             for i in range(depth)
         ])
-        self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
+        self.final_layer = T2IFinalLayer(
+            hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
+        )
 
-    def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs):
+    def forward_orig(self, x, timestep, y, mask=None, data_info=None, **kwargs):
         """
         Original forward pass of PixArt.
         x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
@@ -166,9 +189,6 @@ class PixArtMS(PixArt):
         y: (N, 1, 120, C) tensor of class labels
         """
         bs = x.shape[0]
-        x = x.to(self.dtype)
-        timestep = t.to(self.dtype)
-        y = y.to(self.dtype)
         
         pe_interpolation = self.pe_interpolation
         if pe_interpolation is None or self.pe_precision is not None:
@@ -181,10 +201,10 @@ class PixArtMS(PixArt):
                 self.pos_embed.shape[-1], (self.h, self.w), pe_interpolation=pe_interpolation,
                 base_size=self.base_size
             )
-        ).unsqueeze(0).to(device=x.device, dtype=self.dtype)
+        ).to(device=x.device, dtype=x.dtype).unsqueeze(0)
 
         x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
-        t = self.t_embedder(timestep)  # (N, D)
+        t = self.t_embedder(timestep, x.dtype)  # (N, D)
 
         if self.micro_conditioning:
             c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype)
@@ -212,46 +232,29 @@ class PixArtMS(PixArt):
         
         return x
 
-    def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, **kwargs):
+    def forward(self, x, timesteps, context, width=None, height=None, img_hw=None, aspect_ratio=None, **kwargs):
-        """
+        bs, c, h, w = x.shape
-        Forward pass that adapts comfy input to original forward function
+        dtype = self.dtype
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        device = x.device
-        timesteps: (N,) tensor of diffusion timesteps
+
-        context: (N, 1, 120, C) conditioning
-        img_hw: height|width conditioning
-        aspect_ratio: aspect ratio conditioning
-        """
         ## size/ar from cond with fallback based on the latent image shape.
         bs = x.shape[0]
         data_info = {}
         if img_hw is None:
-            data_info["img_hw"] = torch.tensor(
+            data_info["img_hw"] = torch.tensor([h*8, w*8], dtype=dtype, device=device).repeat(bs, 1)
-                [[x.shape[2]*8, x.shape[3]*8]],
-                dtype=self.dtype,
-                device=x.device
-            ).repeat(bs, 1)
         else:
             data_info["img_hw"] = img_hw.to(dtype=x.dtype, device=x.device)
-        if aspect_ratio is None or True:
+        if aspect_ratio is None:
-            data_info["aspect_ratio"] = torch.tensor(
+            data_info["aspect_ratio"] = torch.tensor([h/w], dtype=dtype, device=device).repeat(bs, 1)
-                [[x.shape[2]/x.shape[3]]],
-                dtype=self.dtype,
-                device=x.device
-            ).repeat(bs, 1)
         else:
-            data_info["aspect_ratio"] = aspect_ratio.to(dtype=x.dtype, device=x.device)
+            data_info["aspect_ratio"] = aspect_ratio.to(dtype=dtype, device=device)
 
         ## Still accepts the input w/o that dim but returns garbage
         if len(context.shape) == 3:
             context = context.unsqueeze(1)
 
         ## run original forward pass
-        out = self.forward_raw(
+        out = self.forward_orig(x, timesteps, context, data_info=data_info)
-            x = x.to(self.dtype),
-            t = timesteps.to(self.dtype),
-            y = context.to(self.dtype),
-            data_info=data_info,
-        )
 
         ## only return EPS
         out = out.to(torch.float)